Soft Constraint Propagation and Solving in Constraint Handling Rules

Bistarelli, Stefano; Frühwirth, Thom; Marte, Michael; Rossi, Francesca

doi:10.1111/j.0824-7935.2004.00239.x

Cited by 14 publications

(13 citation statements)

References 13 publications

(23 reference statements)

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“…Here, we broadly follow the conception of a (soft) constraint as put forward in Bistarelli et al (1997), Bistarelli et al (2004), and Dubois et al (1996), which -as pointed out by Dubois et al -is rooted in early work on constraint satisfaction in computer vision (Waltz, 1975;Rosenfeld et al, 1976). The current formulation provides us with a uniform characterization of hard and soft constraints.…”

Section: Formalizing the Notion Of A Constraintmentioning

confidence: 99%

Generating under Global Constraints: the Case of Scripted Dialogue

Piwek

Deemter

2007

Res on Lang and Comput

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Recently, the view of Natural Language Generation (nlg) as a Constraint Satisfaction Problem (csp) has seen something of a revival. The aim of this paper is to examine the issues that arise when nlg is viewed as a csp, and to introduce a novel application of constraint-based nlg, namely the Scripted Dialogue. Scripted Dialogue shares a number of crucial features with discourse, which make it possible to control the global properties of a computer-generated dialogue in the same way as those of a generated discourse. We pay particular attention to the use of soft constraints for enforcing global properties of text and dialogue. Because there has been little research into the formal properties of soft constraints in relation to generation, we start out with a theoretical exploration. We argue that, when multiple constraints are involved, it is important to define properly what is being optimised before proposing specific algorithms, and we argue that such definitions are often lacking in csp-based nlg. We show that it can be difficult (and sometimes even impossible) to guarantee satisfaction of global constraints by following local strategies. Based on these difficulties, we propose a novel approach to the generation of discourse and dialogue which combines csp solving with revision. Scripted Dialogue is used to illustrate this approach, which is compared with alternatives such as monitoring and estimation.

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Section: Formalizing the Notion Of A Constraintmentioning

confidence: 99%

Generating under Global Constraints: the Case of Scripted Dialogue

Piwek

Deemter

2007

Res on Lang and Comput

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“…Unlike hard (required) constraints which must hold in any solution, soft (preferential) constraints must only be satisfied as far as possible. Bistarelli, Frühwirth et al (2004) present a series of constraint solvers for (mostly) extensionally defined finite domain soft constraints, based on the framework of c-semirings. In this framework, soft constraints can be combined and projected onto a subset of their variables, by using the two operators of a c-semiring.…”

Section: Soft Constraints and Schedulingmentioning

confidence: 99%

As time goes by: Constraint Handling Rules

Sneyers¹,

Weert²,

Schrijvers³

et al. 2009

Theory and Practice of Logic Programming

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Constraint Handling Rules (CHR) is a high-level programming language based on multiheaded multiset rewrite rules. Originally designed for writing user-defined constraint solvers, it is now recognized as an elegant general purpose language.CHR-related research has surged during the decade following the previous survey by Frühwirth (1998). Covering more than 180 publications, this new survey provides an overview of recent results in a wide range of research areas, from semantics and analysis to systems, extensions and applications.

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“…For example, approaches such as in Bistarelli et al (1997Bistarelli et al ( , 2001Bistarelli et al ( , 2004; Bistarelli (2004), or a decision diagram approach (Wilson, 2005). Also, a general method for this kind of problem is based on variable elimination.…”

Section: Computational Techniques For the Distributive Lattice Casementioning

confidence: 99%

A logic of soft constraints based on partially ordered preferences

Wilson

2006

J Heuristics

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Representing and reasoning with an agent's preferences is important in many applications of constraints formalisms. Such preferences are often only partially ordered. One class of soft constraints formalisms, semiring-based CSPs, allows a partially ordered set of preference degrees, but this set must form a distributive lattice; whilst this is convenient computationally, it considerably restricts the representational power. This paper constructs a logic of soft constraints where it is only assumed that the set of preference degrees is a partially ordered set, with a maximum element 1 and a minimum element 0. When the partially ordered set is a distributive lattice, this reduces to the idempotent semiring-based CSP approach, and the lattice operations can be used to define a sound and complete proof theory. A generalised possibilistic logic, based on partially ordered values of possibility, is also constructed, and shown to be formally very strongly related to the logic of soft constraints. It is shown how the machinery that exists for the distributive lattice case can be used to perform sound and complete deduction, using a compact embedding of the partially ordered set in a distributive lattice

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Soft Constraint Propagation and Solving in Constraint Handling Rules

Cited by 14 publications

References 13 publications

Generating under Global Constraints: the Case of Scripted Dialogue

Generating under Global Constraints: the Case of Scripted Dialogue

As time goes by: Constraint Handling Rules

A logic of soft constraints based on partially ordered preferences

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